Load Libraries
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## v readr 1.4.0 v forcats 0.5.0## Warning: package 'tibble' was built under R version 4.0.3## Warning: package 'tidyr' was built under R version 4.0.3## Warning: package 'readr' was built under R version 4.0.3## Warning: package 'dplyr' was built under R version 4.0.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
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## layout## Warning: package 'htmlwidgets' was built under R version 4.0.3Load the Data
data <- read.csv("data/thermocouple_actualdata.csv")
data$time <- data$ï..Time
data$Depth <- as.factor(data$Depth)
new.df <- na.omit(data)
new.df <- new.df %>% group_by(Depth, time, Type, Temperature, ID) %>% summarize(Mean_Temp = mean(Temperature.1))## `summarise()` has grouped output by 'Depth', 'time', 'Type', 'Temperature'. You can override using the `.groups` argument.Plot for Bare 450 with the model 450
Plot for Bare 600 with the model 600
Plot for BSC 450 with the model 450
bsc450 <- ggplot(new.df %>% filter(Temperature == "450", Mean_Temp >0, time < 60, Type != "Bare"), aes(x = time, y = Mean_Temp, color = Depth)) + geom_point(alpha = 0.5, size = 0.3)
ggplotly(bsc450)Plot for BSC 600 with the model 600
bsc600 <- ggplot(new.df %>% filter(Temperature == "600", Mean_Temp >0, time < 60, Type != "Bare"), aes(x = time, y = Mean_Temp, color = Depth)) + geom_point(alpha = 0.5, size = 0.3)
ggplotly(bsc600)